Modeling Risk Management for Resources and Environment in China (Computational Risk Management)

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  1. An Approach to Overseas Iron Ore Investment Risk Assessment Based on Fuzzy Neural Network
  2. Profitability. Efficiency. Regulatory compliance.
  3. Global change: Put people at the centre of global risk management : Nature News & Comment

That's because good governance goes beyond compliance and risk avoidance to help ensure optimal business outcomes. Our innovative, risk-based approach to governance enables you to:. Our single, integrated framework facilitates both IFRS 17 and Solvency II compliance, while turning compliance into an opportunity for operational excellence. Are you ready to face the coming challenges? Data is consistent. It is aligned. The new SAS system will help us reduce model risk and increase business value. Experience the power of SAS software yourself.

See SAS software in action with a free demo. Get pricing based on your company's needs. Let us know how we can help you. Risk Management Drive business evolution with intelligent risk analytics. Request demo. Regulatory compliance. Regulatory Risk Management Streamline your credit risk and regulatory capital analytics.

Overview Why SAS? Related Solutions How to Buy Systemic risk is a central concern driving regulatory strategy. SAS can help you stay on top of such changes with solutions that enable you to: Optimize credit risk analysis. Create a consolidated data, modeling and reporting platform. Integrate existing risk models and data hierarchies into a streamlined, unified data infrastructure for measuring and reporting on credit and counterparty risk.

A firmwide risk technology foundation. Update legacy processes with a modernized risk infrastructure that supports scalable, high-quality data, workflow analytics and reporting. High-performance architecture. SAS delivers a modern risk ecosystem — from data management through model execution. Intuitive process flow visualization capabilities, combined with a central repository for documentation, greatly improve quality controls. And you get the power of parallel code execution at a very low cost.

Frequent updates to regulatory content. SAS separates the more dynamic regulatory calculation methodologies and reports from our software platform, enabling you to consume more frequent releases in timely, easy-to-install downloads for updating calculations and reports without having to upgrade the overall system. Flexible visualization capabilities. Related Solutions How to Buy Go beyond regulatory compliance to drive strategy and improve business performance.

SAS delivers superior data, modeling and computational functionality with unmatched analytics and reporting capabilities that enable you to: Operate with greater efficiencies. Relieve the resource burden of stress testing, and reduce cycle times to allow for greater focus on higher-value activities.

Facilitate capital planning.

About this book

Use comprehensive tools and powerful reporting capabilities to uncover risks and opportunities in your portfolio, and use these insights to inform your strategic plan. Expand your capabilities. From running multiple business-specific scenarios to executing reverse stress tests, you can move beyond supervisory stress testing scenarios to gain insights into the path ahead and prepare your portfolio for the unexpected.

Improve transparency and controls. Significantly reduce model risks and improve auditability with flexible workflow automation and embedded controls. Adapt and evolve. Easily introduce new models, methodologies and scenarios as business needs change. Drill down and focus on wherever conditions dictate.

Improve and refine processes over time. Efficient process orchestration. A centrally managed workflow helps you orchestrate entire stress testing and capital planning processes using a web-based interface. Strong controls and auditability. Efficiently manage and monitor well-governed, transparent and auditable stress testing and capital planning processes from a central point of control, with integrated qualitative assessment and governance processes for both internal management and external regulators.

Powerful risk analytics — simplified. A centralized, web-based interface simplifies the development, execution and maintenance of even the most complex models, while enabling you to quickly compare outputs and visually explore results. You can perform advanced scenario-based analysis across all risk types and asset classes with minimal coding required.

Integrated, market-leading model governance. Manage and store all models and scenarios in a centralized library. Easily test and validate models, and orchestrate their deployment to production in a controlled and traceable manner. Comprehensive data management. House easily accessible and integrated data repositories in a transparent and readily searchable form. Trace data lineage throughout the entire stress testing life cycle with market-leading capabilities for data quality management and metadata documentation. Need help? How do I find a book?

Can I borrow this item? Can I get a copy? Can I view this online? Ask a librarian. Members of Aboriginal, Torres Strait Islander and Maori communities are advised that this catalogue contains names and images of deceased people. Book , Online - Google Books. Risk management in sustainable economy pt. Risk management in engineering projects pt. Risk management in sustainable enterprise pt. Environmental risk management pt. Medina M. Sakijege T. Health risks associated with municipal waste combustion on the example of Laskowa commune Southern Poland Hum. Risk Assess. Omololu O. University of Ibadan; Ibadan, Nigeria: Chengula A.

Oberlin A. Resource recovery potential: A case study of household waste in Kinondoni municipality, Dar es Salaam, Tanzania. Wilson D. Role of informal sector in waste management in developing countries. Adefemi S. Sommers L. Variable nature of chemical composition of sewage gouges.

Tanzania: Dar es Salaam City Profile. Regional and Information Office; Nairobi, Kenya: Salon E.

An Approach to Overseas Iron Ore Investment Risk Assessment Based on Fuzzy Neural Network

Linster M. Svarstad H. Land Use Policy. Cao H. European Environment Agency. European Environment Agency; Copenhagen, Denmark: Xie H. The measure and countermeasure of ecological security in the suburban area in Beijing city of Haidian district as an example. China Popul. Zebardast L. Maxim L. Liu Y. Bradley P. Bottero M. Integrating the analytic network process ANP and the driving force-pressure-state-impact-responses DPSIR model for the sustainability assessment of territorial transformations.

Elliott M. The role of the DPSIR approach and conceptual models in marine environmental management: An example for offshore wind power. Shao C. Sun X. Cao Y. Risk Anal. Crisis Response. Roberts T.

Profitability. Efficiency. Regulatory compliance.

Von Schirnding Y. Availability of environmental health services among blacks in urban and peri-urban areas of South Africa. South Afr. Vir Geneeskd. Rodriguez-Labajos B. Multi-Level driving forces of biological invasions. Chaggu E. Excreta Disposal in Dar-es-Salaam. Castro M. Community-Based environmental management for malaria control: Evidence from a small-scale intervention in Dar es Salaam, Tanzania. Scholz R. Life Cycle Assess. Bhattacharyya K. Impact of urbanization on the quality of water in a natural reservoir: A case study with the Deepor Beel in Guwahati city, India.

Water Environ. Deng L. Sichuan Environ. Angelo B. Bengtsson S. Mojzsis S. Eos Trans. Salehi E. Space Ontol. Gheewala S. Carroll R. Beck B. Radionovs A. Saaty T. Volume 1 Kluwer; Dordrecht, The Netherlands: How to make a decision? Application of environmental risk assessment for strategic decision making in coastal areas: Case studies in China. Vaidya S. Analytic hierarchy process: An overview of applications. Kim E. Korean Soc. Hazard Mitig. The Analytic Hierarchy Process. The analytic hierarchy and analytic network measurement processes: Applications to decisions under risk.

Pure Appl. Ishizaka A. Review of the main developments in the analytic hierarchy process. Expert Syst. Kumar R. Raybould A. Syngenta; Basel, Switzerland: Alonso J. Brent A. Application of the analytical hierarchy process to establish health care waste management systems that minimise infection risks in developing countries. The Analytic Hierarch Process in Conflict management. Kelliher P. A common risk classification system for the Actuarial Profession. Adrian V. Sampaio J. Hazardous wastes management in Brazil; the need for a regional synoptic approach. Water Sci. Kaseva M.

Recycling inorganic domestic solid wastes: Results from a pilot study in Dar es Salaam City, Tanzania. Kihampa C. Solomon A. Solid Waste Technol. Zhang Y. Urban city life garbage treatment technology status and management countermeasures. Yhdego M. Macgill S. A New Paradigm for Risk Analysis. University of Leeds; Leeds, UK: Support Center Support Center.

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External link. Please review our privacy policy. Driving Forces. Bio-physiological needs. Protection from hostile environment. Cultural practices. Population below poverty line. Building and Construction. Waste material generated.

Institution and Services. Other Offices. Energy and Material consumption. Material use e. Markets formal and informal. Other major generates. Direction of underground water. Ecosystem services climate regulation and limited recreational opportunities. Social Impacts Human health-related impacts. Economic repercussions. Institutional framework. Policies, Law and regulations. Environmental education and publicity. Environmental governance and Investment. Other environmental management expenses. New approaches and Modern technologies.

Application of Economic instruments EIs. DR-System and bond. A5: Responses. The two compared parameters contribute equally to the referred goal. Experience and judgement slightly favor one parameter over another. Experience and judgement strongly favor one parameter over another. One element is favored very strongly over another, and its domination is demonstrated in practice.

The evidence favoring one parameter over the other is of the highest possible order of confirmation. The referred elements have nearly equal importance. A1: Driving Forces. B3: Belonging. A2: Pressure. B8: Economy. A3: State.

B Pollution level. A4: Impacts. B Economic Impacts. B Environmental governance and investment. B New approaches and Modern technologies. Extremely low. Low external pressure. Good condition, needs to be maintained. Relatively low. Less external pressure. Good condition, vigilance required to avoid further disturbances.

Environmental state is changing with external pressure. Need to work on the changing state. Relatively high. Poor state with large external pressure. Extremely high. Serious damage due to great pressure. Dangerous environment for animals and human living; rehabilitation programs are urgently required. Both, qualitative and quantitative, primary and secondary data, regarding drivers for survival of the Dar es Salaam Community.

Safety needs. Need for Family and community. Population and society. Secondary quantitative data on population. Population density. Population growth rate. Urbanization rate. Number of new buildings. Both, qualitative and quantitative, primary and secondary data. New built-up areas.

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Total covered land. Healthcare facilities HCFs. Both, qualitative and quantitative, primary and secondary data on institutional and service waste. Education services. GDP per-capita. Trend for all years. MSW generation rate. Domestic waste. Business and markets waste. Water bodies and fishing garbage. Waste from Healthcare facilities. Construction and demolition.

Global change: Put people at the centre of global risk management : Nature News & Comment

Industrial waste. MSW management status. Amount recycled.